{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movie Recommendation with Content-Based and Collaborative Filtering\n",
    "“*What movie should I watch this evening?*” \n",
    "\n",
    "Have you ever had to answer this question at least once when you came home from work? As for me — yes, and more than once. From Netflix to Hulu, the need to build robust movie recommendation systems is extremely important given the huge demand for personalized content of modern consumers.\n",
    "\n",
    "An example of recommendation system is such as this:\n",
    "* User A watches **Game of Thrones** and **Breaking Bad**.\n",
    "* User B does search on **Game of Thrones**, then the system suggests **Breaking Bad** from data collected about user A.\n",
    "\n",
    "Recommendation systems are used not only for movies, but on multiple other products and services like Amazon (Books, Items), Pandora/Spotify (Music), Google (News, Search), YouTube (Videos) etc.\n",
    "\n",
    "![netflix](images/netflix.png)\n",
    "\n",
    "Two most ubiquitous types of personalized recommendation systems are **Content-Based** and **Collaborative Filtering**. Collaborative filtering produces recommendations based on the knowledge of users’ attitude to items, that is it uses the “wisdom of the crowd” to recommend items. In contrast, content-based recommendation systems focus on the attributes of the items and give you recommendations based on the similarity between them.\n",
    "\n",
    "In this notebook, I will attempt at implementing these two systems to recommend movies and evaluate them to see which one performs better.\n",
    "\n",
    "After reading this post you will know:\n",
    "\n",
    "* About the MovieLens dataset problem for recommender system.\n",
    "* How to load and process the data.\n",
    "* How to do exploratory data analysis.\n",
    "* The 2 different types of recommendation engines.\n",
    "* How to develop a content-based recommendation model based on movie genres.\n",
    "* How to develop a collaborative filtering model based on user ratings.\n",
    "* Alternative approach to improve existing models.\n",
    "\n",
    "Let’s get started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The MovieLens Dataset\n",
    "One of the most common datasets that is available on the internet for building a Recommender System is the [MovieLens DataSet](https://grouplens.org/datasets/movielens/). This version of the dataset that I'm working with ([1M](https://grouplens.org/datasets/movielens/1m/)) contains 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000.\n",
    "\n",
    "The data was collected by GroupLens researchers over various periods of time, depending on the size of the set. This 1M version was released on February 2003. Users were selected at random for inclusion. All users selected had rated at least 20 movies. Each user is represented by an id, and no other information is provided.\n",
    "\n",
    "The original data are contained in three files, [movies.dat](https://github.com/khanhnamle1994/movielens/blob/master/dat/movies.dat), [ratings.dat](https://github.com/khanhnamle1994/movielens/blob/master/dat/ratings.dat) and [users.dat](https://github.com/khanhnamle1994/movielens/blob/master/dat/users.dat). To make it easier to work with the data, I converted them into csv files. The process can be viewed in my [Data Processing Notebook](https://github.com/khanhnamle1994/movielens/blob/master/Data_Processing.ipynb).\n",
    "\n",
    "![movielens](images/movielens.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "Let's load this data into Python. I will load the dataset with Pandas onto Dataframes **ratings**, **users**, and **movies**. Before that, I'll also pass in column names for each CSV and read them using pandas (the column names are available in the [Readme](https://github.com/khanhnamle1994/movielens/blob/master/README.md) file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Reading ratings file\n",
    "# Ignore the timestamp column\n",
    "ratings = pd.read_csv('ratings.csv', sep='\\t', encoding='latin-1', usecols=['user_id', 'movie_id', 'rating'])\n",
    "\n",
    "# Reading users file\n",
    "users = pd.read_csv('users.csv', sep='\\t', encoding='latin-1', usecols=['user_id', 'gender', 'zipcode', 'age_desc', 'occ_desc'])\n",
    "\n",
    "# Reading movies file\n",
    "movies = pd.read_csv('movies.csv', sep='\\t', encoding='latin-1', usecols=['movie_id', 'title', 'genres'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets take a peak into the content of each file to understand them better.\n",
    "\n",
    "### Ratings Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   user_id  movie_id  rating\n",
      "0        1      1193       5\n",
      "1        1       661       3\n",
      "2        1       914       3\n",
      "3        1      3408       4\n",
      "4        1      2355       5\n"
     ]
    }
   ],
   "source": [
    "# Check the top 5 rows\n",
    "print(ratings.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000209 entries, 0 to 1000208\n",
      "Data columns (total 3 columns):\n",
      "user_id     1000209 non-null int64\n",
      "movie_id    1000209 non-null int64\n",
      "rating      1000209 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 22.9 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Check the file info\n",
    "print(ratings.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms that there are 1M ratings for different user and movie combinations.\n",
    "\n",
    "### Users Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   user_id gender zipcode  age_desc              occ_desc\n",
      "0        1      F   48067  Under 18          K-12 student\n",
      "1        2      M   70072       56+         self-employed\n",
      "2        3      M   55117     25-34             scientist\n",
      "3        4      M   02460     45-49  executive/managerial\n",
      "4        5      M   55455     25-34                writer\n"
     ]
    }
   ],
   "source": [
    "# Check the top 5 rows\n",
    "print(users.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6040 entries, 0 to 6039\n",
      "Data columns (total 5 columns):\n",
      "user_id     6040 non-null int64\n",
      "gender      6040 non-null object\n",
      "zipcode     6040 non-null object\n",
      "age_desc    6040 non-null object\n",
      "occ_desc    6040 non-null object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 236.0+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Check the file info\n",
    "print(users.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms that there are 6040 users and we have 5 features for each (unique user ID, gender, age, occupation and the zip code they are living in).\n",
    "\n",
    "### Movies Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   movie_id                               title                        genres\n",
      "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
      "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
      "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
      "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
      "4         5  Father of the Bride Part II (1995)                        Comedy\n"
     ]
    }
   ],
   "source": [
    "# Check the top 5 rows\n",
    "print(movies.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3883 entries, 0 to 3882\n",
      "Data columns (total 3 columns):\n",
      "movie_id    3883 non-null int64\n",
      "title       3883 non-null object\n",
      "genres      3883 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 91.1+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Check the file info\n",
    "print(movies.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains attributes of the 3883 movies. There are 3 columns including the movie ID, their titles, and their genres. Genres are pipe-separated and are selected from 18 genres (Action, Adventure, Animation, Children's, Comedy, Crime, Documentary, Drama, Fantasy, Film-Noir, Horror, Musical, Mystery, Romance, Sci-Fi, Thriller, War, Western)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "### Titles\n",
    "Are there certain words that feature more often in Movie Titles? I'll attempt to figure this out using a word-cloud visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103fdfeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import new libraries\n",
    "%matplotlib inline\n",
    "import wordcloud\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "\n",
    "# Create a wordcloud of the movie titles\n",
    "movies['title'] = movies['title'].fillna(\"\").astype('str')\n",
    "title_corpus = ' '.join(movies['title'])\n",
    "title_wordcloud = WordCloud(stopwords=STOPWORDS, background_color='black', height=2000, width=4000).generate(title_corpus)\n",
    "\n",
    "# Plot the wordcloud\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.imshow(title_wordcloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beautiful, isn't it? I can recognize that there are a lot of movie franchises in this dataset, as evidenced by words like *II* and *III*... In addition to that, *Day*, *Love*, *Life*, *Time*, *Night*, *Man*, *Dead*, *American* are among the most commonly occuring words.\n",
    "\n",
    "### Ratings\n",
    "Next I want to examine the **rating** further. Let's take a look at its summary statistics and distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.000209e+06\n",
       "mean     3.581564e+00\n",
       "std      1.117102e+00\n",
       "min      1.000000e+00\n",
       "25%      3.000000e+00\n",
       "50%      4.000000e+00\n",
       "75%      4.000000e+00\n",
       "max      5.000000e+00\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics of rating\n",
    "ratings['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108ee97f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1051875c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import seaborn library\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "sns.set(font_scale=1.5)\n",
    "%matplotlib inline\n",
    "\n",
    "# Display distribution of rating\n",
    "sns.distplot(ratings['rating'].fillna(ratings['rating'].median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that users are quite generous in their ratings. The mean rating is 3.58 on a scale of 5. Half the movies have a rating of 4 and 5. I personally think that a 5-level rating skill wasn’t a good indicator as people could have different rating styles (i.e. person A could always use 4 for an average movie, whereas person B only gives 4 out for their favorites). Each user rated at least 20 movies, so I doubt the distribution could be caused just by chance variance in the quality of movies.\n",
    "\n",
    "Let's also take a look at a subset of 20 movies with the highest rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489283</th>\n",
       "      <td>American Beauty (1999)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489259</th>\n",
       "      <td>Election (1999)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489257</th>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "      <td>Action|Sci-Fi|Thriller</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489256</th>\n",
       "      <td>Dead Ringers (1988)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489237</th>\n",
       "      <td>Rushmore (1998)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489236</th>\n",
       "      <td>Simple Plan, A (1998)</td>\n",
       "      <td>Crime|Thriller</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489226</th>\n",
       "      <td>Hands on a Hard Body (1996)</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489224</th>\n",
       "      <td>Pleasantville (1998)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489212</th>\n",
       "      <td>Say Anything... (1989)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489207</th>\n",
       "      <td>Beetlejuice (1988)</td>\n",
       "      <td>Comedy|Fantasy</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489190</th>\n",
       "      <td>Roger &amp; Me (1989)</td>\n",
       "      <td>Comedy|Documentary</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489172</th>\n",
       "      <td>Buffalo 66 (1998)</td>\n",
       "      <td>Action|Comedy|Drama</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489171</th>\n",
       "      <td>Out of Sight (1998)</td>\n",
       "      <td>Action|Crime|Romance</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489170</th>\n",
       "      <td>I Went Down (1997)</td>\n",
       "      <td>Action|Comedy|Crime</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489168</th>\n",
       "      <td>Opposite of Sex, The (1998)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489157</th>\n",
       "      <td>Good Will Hunting (1997)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489152</th>\n",
       "      <td>Fast, Cheap &amp; Out of Control (1997)</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489149</th>\n",
       "      <td>L.A. Confidential (1997)</td>\n",
       "      <td>Crime|Film-Noir|Mystery|Thriller</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489145</th>\n",
       "      <td>Contact (1997)</td>\n",
       "      <td>Drama|Sci-Fi</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      title                            genres  \\\n",
       "0                          Toy Story (1995)       Animation|Children's|Comedy   \n",
       "489283               American Beauty (1999)                      Comedy|Drama   \n",
       "489259                      Election (1999)                            Comedy   \n",
       "489257                   Matrix, The (1999)            Action|Sci-Fi|Thriller   \n",
       "489256                  Dead Ringers (1988)                    Drama|Thriller   \n",
       "489237                      Rushmore (1998)                            Comedy   \n",
       "489236                Simple Plan, A (1998)                    Crime|Thriller   \n",
       "489226          Hands on a Hard Body (1996)                       Documentary   \n",
       "489224                 Pleasantville (1998)                            Comedy   \n",
       "489212               Say Anything... (1989)              Comedy|Drama|Romance   \n",
       "489207                   Beetlejuice (1988)                    Comedy|Fantasy   \n",
       "489190                    Roger & Me (1989)                Comedy|Documentary   \n",
       "489172                    Buffalo 66 (1998)               Action|Comedy|Drama   \n",
       "489171                  Out of Sight (1998)              Action|Crime|Romance   \n",
       "489170                   I Went Down (1997)               Action|Comedy|Crime   \n",
       "489168          Opposite of Sex, The (1998)                      Comedy|Drama   \n",
       "489157             Good Will Hunting (1997)                             Drama   \n",
       "489152  Fast, Cheap & Out of Control (1997)                       Documentary   \n",
       "489149             L.A. Confidential (1997)  Crime|Film-Noir|Mystery|Thriller   \n",
       "489145                       Contact (1997)                      Drama|Sci-Fi   \n",
       "\n",
       "        rating  \n",
       "0            5  \n",
       "489283       5  \n",
       "489259       5  \n",
       "489257       5  \n",
       "489256       5  \n",
       "489237       5  \n",
       "489236       5  \n",
       "489226       5  \n",
       "489224       5  \n",
       "489212       5  \n",
       "489207       5  \n",
       "489190       5  \n",
       "489172       5  \n",
       "489171       5  \n",
       "489170       5  \n",
       "489168       5  \n",
       "489157       5  \n",
       "489152       5  \n",
       "489149       5  \n",
       "489145       5  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join all 3 files into one dataframe\n",
    "dataset = pd.merge(pd.merge(movies, ratings),users)\n",
    "# Display 20 movies with highest ratings\n",
    "dataset[['title','genres','rating']].sort_values('rating', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genres\n",
    "The genres variable will surely be important while building the recommendation engines since it describes the content of the film (i.e. Animation, Horror, Sci-Fi). A basic assumption is that films in the same genre should have similar contents. I'll attempt to see exactly which genres are the most popular."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Drama', 1603],\n",
       " ['Comedy', 1200],\n",
       " ['Action', 503],\n",
       " ['Thriller', 492],\n",
       " ['Romance', 471]]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a census of the genre keywords\n",
    "genre_labels = set()\n",
    "for s in movies['genres'].str.split('|').values:\n",
    "    genre_labels = genre_labels.union(set(s))\n",
    "\n",
    "# Function that counts the number of times each of the genre keywords appear\n",
    "def count_word(dataset, ref_col, census):\n",
    "    keyword_count = dict()\n",
    "    for s in census: \n",
    "        keyword_count[s] = 0\n",
    "    for census_keywords in dataset[ref_col].str.split('|'):        \n",
    "        if type(census_keywords) == float and pd.isnull(census_keywords): \n",
    "            continue        \n",
    "        for s in [s for s in census_keywords if s in census]: \n",
    "            if pd.notnull(s): \n",
    "                keyword_count[s] += 1\n",
    "    #______________________________________________________________________\n",
    "    # convert the dictionary in a list to sort the keywords by frequency\n",
    "    keyword_occurences = []\n",
    "    for k,v in keyword_count.items():\n",
    "        keyword_occurences.append([k,v])\n",
    "    keyword_occurences.sort(key = lambda x:x[1], reverse = True)\n",
    "    return keyword_occurences, keyword_count\n",
    "\n",
    "# Calling this function gives access to a list of genre keywords which are sorted by decreasing frequency\n",
    "keyword_occurences, dum = count_word(movies, 'genres', genre_labels)\n",
    "keyword_occurences[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top 5 genres are, in that respect order: Drama, Comedy, Action, Thriller, and Romance. I'll show this on a wordcloud too in order to make it more visually appealing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1051872e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the dictionary used to produce the genre wordcloud\n",
    "genres = dict()\n",
    "trunc_occurences = keyword_occurences[0:18]\n",
    "for s in trunc_occurences:\n",
    "    genres[s[0]] = s[1]\n",
    "\n",
    "# Create the wordcloud\n",
    "genre_wordcloud = WordCloud(width=1000,height=400, background_color='white')\n",
    "genre_wordcloud.generate_from_frequencies(genres)\n",
    "\n",
    "# Plot the wordcloud\n",
    "f, ax = plt.subplots(figsize=(16, 8))\n",
    "plt.imshow(genre_wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types of Recommendation Engines\n",
    "\n",
    "### 1. Content-Based\n",
    "The Content-Based Recommender relies on the similarity of the items being recommended. The basic idea is that if you like an item, then you will also like a “similar” item. It generally works well when it's easy to determine the context/properties of each item.\n",
    "\n",
    "A content based recommender works with data that the user provides, either explicitly movie ratings for the MovieLens dataset. Based on that data, a user profile is generated, which is then used to make suggestions to the user. As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate.\n",
    "\n",
    "### 2. Collaborative Filtering\n",
    "The Collaborative Filtering Recommender is entirely based on the past behavior and not on the context. More specifically, it is based on the similarity in preferences, tastes and choices of two users. It analyses how similar the tastes of one user is to another and makes recommendations on the basis of that. \n",
    "\n",
    "For instance, if user A likes movies 1, 2, 3 and user B likes movies 2,3,4, then they have similar interests and A should like movie 4 and B should like movie 1. This makes it one of the most commonly used algorithm as it is not dependent on any additional information.\n",
    "\n",
    "In general, collaborative filtering is the workhorse of recommender engines. The algorithm has a very interesting property of being able to do feature learning on its own, which means that it can start to learn for itself what features to use. It can be divided into **Memory-Based Collaborative Filtering** and **Model-Based Collaborative filtering**. In this post, I'll only focus on the Memory-Based Collaborative Filtering technique.\n",
    "\n",
    "![rec-systems](images/rec-systems.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content-Based Recommendation Model\n",
    "### Theory\n",
    "The concepts of **Term Frequency (TF)** and **Inverse Document Frequency (IDF)** are used in information retrieval systems and also content based filtering mechanisms (such as a content based recommender). They are used to determine the relative importance of a document / article / news item / movie etc.\n",
    "\n",
    "TF is simply the frequency of a word in a document. IDF is the inverse of the document frequency among the whole corpus of documents. TF-IDF is used mainly because of two reasons: Suppose we search for “**the results of latest European Socccer games**” on Google. It is certain that “**the**” will occur more frequently than “**soccer games**” but the relative importance of **soccer games** is higher than the search query point of view. In such cases, TF-IDF weighting negates the effect of high frequency words in determining the importance of an item (document).\n",
    "\n",
    "Below is the equation to calculate the TF-IDF score:\n",
    "![tfidf](images/tfidf.jpg)\n",
    "\n",
    "After calculating TF-IDF scores, how do we determine which items are closer to each other, rather closer to the user profile? This is accomplished using the **Vector Space Model** which computes the proximity based on the angle between the vectors. In this model, each item is stored as a vector of its attributes (which are also vectors) in an **n-dimensional space** and the angles between the vectors are calculated to **determine the similarity between the vectors**. Next, the user profile vectors are also created based on his actions on previous attributes of items and the similarity between an item and a user is also determined in a similar way.\n",
    "\n",
    "![vector-space](images/vector_space.png)\n",
    "\n",
    "Sentence 2 is more likely to be using Term 2 than using Term 1. Vice-versa for Sentence 1. The method of calculating this relative measure is calculated by taking the cosine of the angle between the sentences and the terms. The ultimate reason behind using cosine is that the **value of cosine will increase with decreasing value of the angle** between which signifies more similarity. The vectors are length normalized after which they become vectors of length 1 and then the cosine calculation is simply the sum-product of vectors.\n",
    "\n",
    "### Implementation\n",
    "With all that theory in mind, I am going to build a Content-Based Recommendation Engine that computes similarity between movies based on movie genres. It will suggest movies that are most similar to a particular movie based on its genre. To do so, I will make use of the file **movies.csv**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Break up the big genre string into a string array\n",
    "movies['genres'] = movies['genres'].str.split('|')\n",
    "# Convert genres to string value\n",
    "movies['genres'] = movies['genres'].fillna(\"\").astype('str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I do not have a quantitative metric to judge our machine's performance so this will have to be done qualitatively. In order to do so, I'll use **TfidfVectorizer** function from **scikit-learn**, which transforms text to feature vectors that can be used as input to estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3883, 127)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tf = TfidfVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\n",
    "tfidf_matrix = tf.fit_transform(movies['genres'])\n",
    "tfidf_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will be using the **[Cosine Similarity](https://masongallo.github.io/machine/learning,/python/2016/07/29/cosine-similarity.html)** to calculate a numeric quantity that denotes the similarity between two movies. Since we have used the TF-IDF Vectorizer, calculating the Dot Product will directly give us the Cosine Similarity Score. Therefore, we will use sklearn's **linear_kernel** instead of cosine_similarities since it is much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.14193614, 0.09010857, 0.1056164 ],\n",
       "       [0.14193614, 1.        , 0.        , 0.        ],\n",
       "       [0.09010857, 0.        , 1.        , 0.1719888 ],\n",
       "       [0.1056164 , 0.        , 0.1719888 , 1.        ]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
    "cosine_sim[:4, :4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I now have a pairwise cosine similarity matrix for all the movies in the dataset. The next step is to write a function that returns the 20 most similar movies based on the cosine similarity score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Build a 1-dimensional array with movie titles\n",
    "titles = movies['title']\n",
    "indices = pd.Series(movies.index, index=movies['title'])\n",
    "\n",
    "# Function that get movie recommendations based on the cosine similarity score of movie genres\n",
    "def genre_recommendations(title):\n",
    "    idx = indices[title]\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:21]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    return titles.iloc[movie_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try and get the top recommendations for a few movies and see how good the recommendations are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25                                        Othello (1995)\n",
       "26                                   Now and Then (1995)\n",
       "29     Shanghai Triad (Yao a yao yao dao waipo qiao) ...\n",
       "30                                Dangerous Minds (1995)\n",
       "35                               Dead Man Walking (1995)\n",
       "39                       Cry, the Beloved Country (1995)\n",
       "42                                    Restoration (1995)\n",
       "52                                       Lamerica (1994)\n",
       "54                                        Georgia (1995)\n",
       "56                          Home for the Holidays (1995)\n",
       "61                             Mr. Holland's Opus (1995)\n",
       "66                                       Two Bits (1995)\n",
       "77                            Crossing Guard, The (1995)\n",
       "79          White Balloon, The (Badkonake Sefid ) (1995)\n",
       "81                       Antonia's Line (Antonia) (1995)\n",
       "82       Once Upon a Time... When We Were Colored (1995)\n",
       "89                    Journey of August King, The (1995)\n",
       "92                                Beautiful Girls (1996)\n",
       "95                               Hate (Haine, La) (1995)\n",
       "112                             Margaret's Museum (1995)\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_recommendations('Good Will Hunting (1997)').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1050               Aladdin and the King of Thieves (1996)\n",
       "2072                             American Tail, An (1986)\n",
       "2073           American Tail: Fievel Goes West, An (1991)\n",
       "2285                            Rugrats Movie, The (1998)\n",
       "2286                                 Bug's Life, A (1998)\n",
       "3045                                   Toy Story 2 (1999)\n",
       "3542                                Saludos Amigos (1943)\n",
       "3682                                   Chicken Run (2000)\n",
       "3685       Adventures of Rocky and Bullwinkle, The (2000)\n",
       "236                                 Goofy Movie, A (1995)\n",
       "12                                           Balto (1995)\n",
       "241                               Gumby: The Movie (1995)\n",
       "310                             Swan Princess, The (1994)\n",
       "592                                      Pinocchio (1940)\n",
       "612                                Aristocats, The (1970)\n",
       "700                               Oliver & Company (1988)\n",
       "876     Land Before Time III: The Time of the Great Gi...\n",
       "1010          Winnie the Pooh and the Blustery Day (1968)\n",
       "1012                       Sword in the Stone, The (1963)\n",
       "1020                        Fox and the Hound, The (1981)\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_recommendations('Toy Story (1995)').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "461            Heaven & Earth (1993)\n",
       "1204        Full Metal Jacket (1987)\n",
       "1214     Boat, The (Das Boot) (1981)\n",
       "1222                    Glory (1989)\n",
       "1545                G.I. Jane (1997)\n",
       "1959      Saving Private Ryan (1998)\n",
       "2358       Thin Red Line, The (1998)\n",
       "2993         Longest Day, The (1962)\n",
       "3559            Flying Tigers (1942)\n",
       "3574    Fighting Seabees, The (1944)\n",
       "3585    Guns of Navarone, The (1961)\n",
       "3684             Patriot, The (2000)\n",
       "40                Richard III (1995)\n",
       "153            Beyond Rangoon (1995)\n",
       "332         Walking Dead, The (1995)\n",
       "523          Schindler's List (1993)\n",
       "641        Courage Under Fire (1996)\n",
       "967          Nothing Personal (1995)\n",
       "979           Michael Collins (1996)\n",
       "1074                  Platoon (1986)\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_recommendations('Saving Private Ryan (1998)').head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, I have quite a decent list of recommendation for **Good Will Hunting** (Drama), **Toy Story** (Animation, Children's, Comedy), and **Saving Private Ryan** (Action, Thriller, War).\n",
    "\n",
    "Overall, here are the pros of using content-based recommendation:\n",
    "* No need for data on other users, thus no cold-start or sparsity problems.\n",
    "* Can recommend to users with unique tastes.\n",
    "* Can recommend new & unpopular items.\n",
    "* Can provide explanations for recommended items by listing content-features that caused an item to be recommended (in this case, movie genres)\n",
    "\n",
    "However, there are some cons of using this approach:\n",
    "* Finding the appropriate features is hard.\n",
    "* Does not recommend items outside a user's content profile.\n",
    "* Unable to exploit quality judgments of other users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collaborative Filtering Recommendation Model\n",
    "The content based engine suffers from some severe limitations. It is only capable of suggesting movies which are close to a certain movie. That is, it is not capable of capturing tastes and providing recommendations across genres.\n",
    "\n",
    "Also, the engine that we built is not really personal in that it doesn't capture the personal tastes and biases of a user. Anyone querying our engine for recommendations based on a movie will receive the same recommendations for that movie, regardless of who she/he is.\n",
    "\n",
    "Therefore, in this section, I will use Memory-Based Collaborative Filtering to make recommendations to movie users. The technique is based on the idea that users similar to a me can be used to predict how much I will like a particular product or service those users have used/experienced but I have not.\n",
    "\n",
    "### Theory\n",
    "There are 2 main types of memory-based collaborative filtering algorithms:\n",
    "1. **User-User Collaborative Filtering**: Here we find look alike users based on similarity and recommend movies which first user’s look-alike has chosen in past. This algorithm is very effective but takes a lot of time and resources. It requires to compute every user pair information which takes time. Therefore, for big base platforms, this algorithm is hard to implement without a very strong parallelizable system.\n",
    "2. **Item-Item Collaborative Filtering**: It is quite similar to previous algorithm, but instead of finding user's look-alike, we try finding movie's look-alike. Once we have movie's look-alike matrix, we can easily recommend alike movies to user who have rated any movie from the dataset. This algorithm is far less resource consuming than user-user collaborative filtering. Hence, for a new user, the algorithm takes far lesser time than user-user collaborate as we don’t need all similarity scores between users. And with fixed number of movies, movie-movie look alike matrix is fixed over time.\n",
    "\n",
    "![user_item_cf](images/user_item_cf.jpg)\n",
    "\n",
    "In either scenario, we builds a similarity matrix. For user-user collaborative filtering, the **user-similarity matrix** will consist of some distance metrics that measure the similarity between any two pairs of users. Likewise, the **item-similarity matrix** will measure the similarity between any two pairs of items.\n",
    "\n",
    "There are 3 distance similarity metrics that are usually used in collaborative filtering:\n",
    "1. **Jaccard Similarity**:\n",
    "    * Similarity is based on the number of users which have rated item A and B divided by the number of users who have rated either A or B\n",
    "    * It is typically used where we don’t have a numeric rating but just a boolean value like a product being bought or an add being clicked\n",
    "\n",
    "2. **Cosine Similarity**: (as in the Content-Based system)\n",
    "    * Similarity is the cosine of the angle between the 2 vectors of the item vectors of A and B\n",
    "    * Closer the vectors, smaller will be the angle and larger the cosine\n",
    "\n",
    "3. **Pearson Similarity**:\n",
    "    * Similarity is the pearson coefficient between the two vectors.\n",
    "\n",
    "For the purpose of diversity, I will use **Pearson Similarity** in this implementation.\n",
    "\n",
    "### Implementation\n",
    "I will use the file **ratings.csv** first as it contains User ID, Movie IDs and Ratings. These three elements are all I need for determining the similarity of the users based on their ratings for a particular movie.\n",
    "\n",
    "First I do some quick data processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fill NaN values in user_id and movie_id column with 0\n",
    "ratings['user_id'] = ratings['user_id'].fillna(0)\n",
    "ratings['movie_id'] = ratings['movie_id'].fillna(0)\n",
    "\n",
    "# Replace NaN values in rating column with average of all values\n",
    "ratings['rating'] = ratings['rating'].fillna(ratings['rating'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the limited computing power in my laptop, I will build the recommender system using only a subset of the ratings. In particular, I will take a random sample of 20,000 ratings (2%) from the 1M ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20004 entries, 562967 to 933367\n",
      "Data columns (total 3 columns):\n",
      "user_id     20004 non-null int64\n",
      "movie_id    20004 non-null int64\n",
      "rating      20004 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 625.1 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Randomly sample 1% of the ratings dataset\n",
    "small_data = ratings.sample(frac=0.02)\n",
    "# Check the sample info\n",
    "print(small_data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I use the **scikit-learn library** to split the dataset into testing and training.  **Cross_validation.train_test_split** shuffles and splits the data into two datasets according to the percentage of test examples, which in this case is 0.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import cross_validation as cv\n",
    "train_data, test_data = cv.train_test_split(small_data, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I need to create a user-item matrix. Since I have splitted the data into testing and training, I need to create two matrices. The training matrix contains 80% of the ratings and the testing matrix contains 20% of the ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16003, 3)\n",
      "(4001, 3)\n"
     ]
    }
   ],
   "source": [
    "# Create two user-item matrices, one for training and another for testing\n",
    "train_data_matrix = train_data.as_matrix(columns = ['user_id', 'movie_id', 'rating'])\n",
    "test_data_matrix = test_data.as_matrix(columns = ['user_id', 'movie_id', 'rating'])\n",
    "\n",
    "# Check their shape\n",
    "print(train_data_matrix.shape)\n",
    "print(test_data_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I use the **pairwise_distances** function from sklearn to calculate the [Pearson Correlation Coefficient](https://stackoverflow.com/questions/1838806/euclidean-distance-vs-pearson-correlation-vs-cosine-similarity). This method provides a safe way to take a distance matrix as input, while preserving compatibility with many other algorithms that take a vector array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.9762515  0.9303185  0.99137913]\n",
      " [0.9762515  1.         0.82877143 0.93945018]\n",
      " [0.9303185  0.82877143 1.         0.97035192]\n",
      " [0.99137913 0.93945018 0.97035192 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "\n",
    "# User Similarity Matrix\n",
    "user_correlation = 1 - pairwise_distances(train_data, metric='correlation')\n",
    "user_correlation[np.isnan(user_correlation)] = 0\n",
    "print(user_correlation[:4, :4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.         -0.00586729  0.02494105]\n",
      " [-0.00586729  1.         -0.05788492]\n",
      " [ 0.02494105 -0.05788492  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Item Similarity Matrix\n",
    "item_correlation = 1 - pairwise_distances(train_data_matrix.T, metric='correlation')\n",
    "item_correlation[np.isnan(item_correlation)] = 0\n",
    "print(item_correlation[:4, :4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the similarity matrix in hand, I can now predict the ratings that were not included with the data. Using these predictions, I can then compare them with the test data to attempt to validate the quality of our recommender model.\n",
    "\n",
    "For the user-user CF case, I will look at the similarity between 2 users (A and B, for example) as weights that are multiplied by the ratings of a similar user B (corrected for the average rating of that user). I also need to normalize it so that the ratings stay between 1 and 5 and, as a final step, sum the average ratings for the user that I am trying to predict. The idea here is that some users may tend always to give high or low ratings to all movies. The relative difference in the ratings that these users give is more important than the absolute values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to predict ratings\n",
    "def predict(ratings, similarity, type='user'):\n",
    "    if type == 'user':\n",
    "        mean_user_rating = ratings.mean(axis=1)\n",
    "        # Use np.newaxis so that mean_user_rating has same format as ratings\n",
    "        ratings_diff = (ratings - mean_user_rating[:, np.newaxis])\n",
    "        pred = mean_user_rating[:, np.newaxis] + similarity.dot(ratings_diff) / np.array([np.abs(similarity).sum(axis=1)]).T\n",
    "    elif type == 'item':\n",
    "        pred = ratings.dot(similarity) / np.array([np.abs(similarity).sum(axis=1)])\n",
    "    return pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation\n",
    "There are many evaluation metrics but one of the most popular metric used to evaluate accuracy of predicted ratings is **Root Mean Squared Error (RMSE)**. I will use the **mean_square_error (MSE)** function from sklearn, where the RMSE is just the square root of MSE.\n",
    "\n",
    "$$\\mathit{RMSE} =\\sqrt{\\frac{1}{N} \\sum (x_i -\\hat{x_i})^2}$$\n",
    "\n",
    "I'll use the scikit-learn's **mean squared error** function as my validation metric. Comparing user- and item-based collaborative filtering, it looks like user-based collaborative filtering gives a better result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "# Function to calculate RMSE\n",
    "def rmse(pred, actual):\n",
    "    # Ignore nonzero terms.\n",
    "    pred = pred[actual.nonzero()].flatten()\n",
    "    actual = actual[actual.nonzero()].flatten()\n",
    "    return sqrt(mean_squared_error(pred, actual))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User-based CF RMSE: 1447.6814769930884\n",
      "Item-based CF RMSE: 1678.7256599700413\n"
     ]
    }
   ],
   "source": [
    "# Predict ratings on the training data with both similarity score\n",
    "user_prediction = predict(train_data_matrix, user_correlation, type='user')\n",
    "item_prediction = predict(train_data_matrix, item_correlation, type='item')\n",
    "\n",
    "# RMSE on the test data\n",
    "print('User-based CF RMSE: ' + str(rmse(user_prediction, test_data_matrix)))\n",
    "print('Item-based CF RMSE: ' + str(rmse(item_prediction, test_data_matrix)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User-based CF RMSE: 699.9584792778463\n",
      "Item-based CF RMSE: 114.97271725933925\n"
     ]
    }
   ],
   "source": [
    "# RMSE on the train data\n",
    "print('User-based CF RMSE: ' + str(rmse(user_prediction, train_data_matrix)))\n",
    "print('Item-based CF RMSE: ' + str(rmse(item_prediction, train_data_matrix)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE of training of model is a metric which measure how much the signal and the noise is explained by the model. I noticed that my RMSE is quite big. I suppose I might have overfitted the training data.\n",
    "\n",
    "Overall, Memory-based Collaborative Filtering is easy to implement and produce reasonable prediction quality. However, there are some drawback of this approach:\n",
    "\n",
    "* It doesn't address the well-known cold-start problem, that is when new user or new item enters the system. \n",
    "* It can't deal with sparse data, meaning it's hard to find users that have rated the same items.\n",
    "* It suffers when new users or items that don't have any ratings enter the system.\n",
    "* It tends to recommend popular items."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternative Approach\n",
    "As I mentioned above, it looks like my Collaborative Filtering model suffers from overfitting problem as I only train it on a small sample dataset (2% of the actual 1M ratings). In order to deal with this, I need to apply dimensionality reduction techniques to capture more signals from the big dataset. Thus comes the use of **low-dimensional factor models (aka, Model-Based Collaborative Filtering)**. I won't be able to implement this approach in this notebook due to computing limit, however, I want to introduce it here to give you a general sense of its advantages.\n",
    "\n",
    "In this approach, CF models are developed using machine learning algorithms to predict user’s rating of unrated items. It has been shown that Model-based Collaborative Filtering has received greater exposure in industry research, mainly as an unsupervised learning method for latent variable decomposition and dimensionality reduction. An example is the competition to win the [Netflix Prize](https://en.wikipedia.org/wiki/Netflix_Prize), which used the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films.\n",
    "\n",
    "Matrix factorization is widely used for recommender systems where it can deal better with scalability and sparsity than Memory-based CF. The goal of MF is to learn the latent preferences of users and the latent attributes of items from known ratings (learn features that describe the characteristics of ratings) to then predict the unknown ratings through the dot product of the latent features of users and items. As per my understanding, the algorithms in this approach can further be broken down into 3 sub-types:\n",
    "\n",
    "* **Matrix Factorization (MF)**: The idea behind such models is that attitudes or preferences of a user can be determined by a small number of hidden latent factors. These factors are also called **Embeddings**, which represent different characteristics for users and items. Matrix factorization can be done by various methods including Support Vecot Decomposition (SVD), Probabilistic Matrix Factorization (PMF), and Non-Negative Matrix Factorization (NMF).\n",
    "\n",
    "* **Clustering based algorithm (KNN)**: The idea of clustering is same as that of memory-based recommendation systems. In memory-based algorithms, we use the similarities between users and/or items and use them as weights to predict a rating for a user and an item. The difference is that the similarities in this approach are calculated based on an unsupervised learning model, rather than Pearson correlation or cosine similarity.\n",
    "\n",
    "* **Neural Nets / Deep Learning**: The idea of using Neural Nets is similar to that of Model-Based Matrix Factorization. In matrix factorizaion, we decompose our original sparse matrix into product of 2 low rank orthogonal matrices. For neural net implementation, we don’t need them to be orthogonal, we want our model to learn the values of embedding matrix itself. The user latent features and movie latent features are looked up from the embedding matrices for specific movie-user combination. These are the input values for further linear and non-linear layers. We can pass this input to multiple relu, linear or sigmoid layers and learn the corresponding weights by any optimization algorithm (Adam, SGD, etc.).\n",
    "\n",
    "![memory-model-cf](images/memory-model-cf.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "In this post, I introduced the Movie Lens dataset for building movie recommendation system.\n",
    "\n",
    "Specifically, I have developed recommendation models including:\n",
    "\n",
    "* How to load and review the data.\n",
    "* How to develop a content-based recommendation model based on movie genres.\n",
    "* How to develop a memory-based collaborative filtering model based on user ratings.\n",
    "* A glimpse at model-based collaborative filtering models as alternative options."
   ]
  }
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